Power of Multi-Modality Variables in Predicting Parkinson's Disease Progression.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The increasing demand for high-accuracy forecasts of disease progression has led to a surge in research employing multi-modality variables for prediction. In this review, we selected articles published from 2016 through June 2024, adhering strictly to our exclusion-inclusion criteria. These articles employed a minimum of two types of variables, including clinical, genetic, biomarker, and neuroimaging modalities. We conducted a comprehensive review and discussion on the application of multi-modality approaches in predicting PD progression. The predictive mechanisms, advantages, and shortcomings of relevant key modalities in predicting PD progression are discussed in the paper. The findings suggest that integrating multiple modalities resulted in more accurate predictions compared to those of fewer modalities in similar conditions. Furthermore, we identified some limitations in the existing field. Future studies that harness advancements in multi-modality variables and machine learning algorithms can mitigate these limitations and enhance predictive accuracy in PD progression.

Spectral reflectance reconstruction based on wideband multi-illuminant imaging and a modified particle swarm optimization algorithm.

A method for spectral reflectance factor reconstruction based on wideband multi-illuminant imaging was proposed, using a programmable LED lighting system and modified Bare Bones Particle Swarm Optimization algorithms. From a set of 16 LEDs with different spectral power distributions, nine light sources with correlated color temperatures in the range of 1924 K - 15746 K, most of them daylight simulators, were generated. Samples from three color charts (X-Rite ColorChecker Digital SG, SCOCIE ScoColor paint chart, and SCOCIE ScoColor textile chart), were captured by a color industrial camera under the nine light sources, and used in sequence as training and/or testing colors. The spectral reconstruction models achieved under multi-illuminant imaging were trained and tested using the canonical Bare Bones Particle Swarm Optimization and its proposed modifications, along with six additional and commonly used algorithms. The impacts of different illuminants, illuminant combinations, algorithms, and training colors on reconstruction accuracy were studied comprehensively. The results indicated that training colors covering larger regions of color space give more accurate reconstructions of spectral reflectance factors, and combinations of two illuminants with a large difference of correlated color temperature achieve more than twice the accuracy of that under a single illuminant. Specifically, the average reconstruction error by the method proposed in this paper for patches from two color charts under A + D90 light sources was 0.94 and 1.08 CIEDE2000 color difference units. The results of the experiment also confirmed that some reconstruction algorithms are unsuitable for predicting spectral reflectance factors from multi-illuminant images due to the complexity of optimization problems and insufficient accuracy. The proposed reconstruction method has many advantages, such as being simple in operation, with no requirement of prior knowledge, and easy to implement in non-contact color measurement and color reproduction devices.

Generation of leaky mode resonance by metallic oxide nanocoating in tilted fiber-optic gratings.

This work investigates the excitation of dense comb-like enhanced leaky mode resonance (eLMR) in tilted fiber Bragg grating (TFBG) integrated with indium tin oxide (ITO) nanocoating. The ITO overlay leads to a large reduction in mode loss and a great increase of propagation length for s-polarized leaky modes, which means the leaky modes become guided. The guidance of leaky modes enhances significantly the interaction with the core guided mode, which leads to the generation of strong dense comb-like eLMR. The results show that the ultra-narrow eLMR bands present promising sensing performance with an extended measurement range and provide advantages of high Q measurement over the case of surface plasmon resonance (SPR) and lossy mode resonance (LMR). The similarities and differences between the eLMR and SPR and LMR are also discussed. This study offers new opportunities to develop eLMR-based multifunctional fiber-optic devices with high performance.

Tuning the resonance of polarization-degenerate LP1,l cladding mode in excessively tilted long period fiber grating for highly sensitive refractive index sensing.

This work presents a sensitive refractive index sensor based on the dual resonance of an excessively tilted long period fiber grating (Ex-TLPFG). The Ex-TLPFG is tuned to couple the guided core mode with only the polarization-degenerate cladding mode LP1,l, which consists of TE/TM0,l and HE2,l vector modes. It is found that the p-polarized LP1,lp mode exhibits a higher sensitivity than that of the s-polarized LP1,ls mode. An optimized sensitivity as high as 12182.9 nm/RIU is achieved for the p-polarized LP1,2p mode at the low refractive index region by tuning the initial resonance condition. The sensing performance is also evaluated through the power measurement method for a single resonance band. It is demonstrated that the improved sensitivity in this work for diameter-reduced Ex-TLPFG is much higher than that for the conventional LPFG based devices, which makes this sensing platform very attractive for a variety of index sensing applications.

Compact self-cascaded KTA-OPO for 2.6 µm laser generation.

We reported a compact self-cascaded KTA-OPO source for 2.6 µm coherent light generation. The OPO is driven in a diode end-pumped and Q-switched Nd:YVO4 laser cavity. Two OPO processes occurred in the same KTA crystal with non-critical phase matching. At an incident diode pump power of 8.7 W and a pulse repetition frequency of 60 kHz, the OPO can generate a maximum average output power of 445 mW at 2.59 µm. The slope efficiency was about 12.7%, and the power fluctuation was less than 8%. Therefore, the self-cascade OPO based on KTA offers a promise scheme for the rugged and compact mid-infrared 2.6 µm laser generation.

Accurate method for computing correlated color temperature.

For the correlated color temperature (CCT) of a light source to be estimated, a nonlinear optimization problem must be solved. In all previous methods available to compute CCT, the objective function has only been approximated, and their predictions have achieved limited accuracy. For example, different unacceptable CCT values have been predicted for light sources located on the same isotemperature line. In this paper, we propose to compute CCT using the Newton method, which requires the first and second derivatives of the objective function. Following the current recommendation by the International Commission on Illumination (CIE) for the computation of tristimulus values (summations at 1 nm steps from 360 nm to 830 nm), the objective function and its first and second derivatives are explicitly given and used in our computations. Comprehensive tests demonstrate that the proposed method, together with an initial estimation of CCT using Robertson's method [J. Opt. Soc. Am. 58, 1528-1535 (1968)], gives highly accurate predictions below 0.0012 K for light sources with CCTs ranging from 500 K to 106 K.

A consensus successive projections algorithm--multiple linear regression method for analyzing near infrared spectra.

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.

Diffraction theory of high numerical aperture subwavelength circular binary phase Fresnel zone plate.

An analytical model of vector formalism is proposed to investigate the diffraction of high numerical aperture subwavelength circular binary phase Fresnel zone plate (FZP). In the proposed model, the scattering on the FZP's surface, reflection and refraction within groove zones are considered and diffraction fields are calculated using the vector Rayleigh-Sommerfeld integral. The numerical results obtained by the proposed phase thick FZP (TFZP) model show a good agreement with those obtained by the finite-difference time-domain (FDTD) method within the effective extent of etch depth. The optimal etch depths predicted by both methods are approximately equal. The analytical TFZP model is very useful for designing a phase and hybrid amplitude-phase FZP with high-NA and short focal length.